Question

无法使用Intel推理引擎（OpenVINO工具包R.5）优化keras模型

我按照下面的tutorial的建议冻结模型。 keras模型经过培训和测试。我需要对其进行优化以进行推理。但是在自定义模型上运行模型优化器（mo.py脚本）时出现错误。

[ ERROR ] shapes (128,9) and (0,) not aligned: 9 (dim 1) != 0 (dim 0)

我模型的最后几层（9是类的输出数量）是

conv2d_4 (Conv2D) (None, 4, 4, 128) 204928 batch_normalization_3[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 4, 4, 128) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 4, 4, 128) 512 activation_4[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 1, 1, 128) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 1, 1, 128) 0 average_pooling2d_2[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (None, 128) 0 dropout_2[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, 128) 16512 flatten[0][0]
__________________________________________________________________________________________________
activation_5 (Activation) (None, 128) 0 dense[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 128) 512 activation_5[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 128) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 9) 1161 dropout_3[0][0]
__________________________________________________________________________________________________
color_prediction (Activation) (None, 9) 0 dense_1[0][0]
__________________________________________________________________________________________________

由于存在BatchNormalization层，模型优化器失败。当我删除它们时，它运行成功。但是我用

冻结图

tf.keras.backend.set_learning_phase(0)

因此，必须在冻结图中删除诸如BatchNormalization和Dropout之类的节点，我无法弄清为什么不删除它们。

非常感谢！

Answer 1

我设法在具有批处理归一化层的Keras模型上运行OpenVINO模型优化器。该模型似乎收敛得更快。尽管测试分类率比没有BN的模型之一低约5-7％（测试和训练数据集的分类率之间的差距更大）。我不确定解决方案中是否已从模型中正确删除BatchNormalization（但openVINO模型文件不包含该文件，因此已将其删除）。

删除BN和Dropout层：

#Clear any previous session.
tf.keras.backend.clear_session()
#This line must be executed before loading Keras model.
tf.keras.backend.set_learning_phase(0) 
model = tf.keras.models.load_model(weights_path)

for layer in model.layers:
    layer.training = False
    if isinstance(layer, tf.keras.layers.BatchNormalization):
        layer._per_input_updates = {}
    elif isinstance(layer, tf.keras.layers.Dropout):
        layer._per_input_updates = {}

而不是冻结会话：

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
"""
Freezes the state of a session into a pruned computation graph.

Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
pruned so subgraphs that are not necessary to compute the requested
outputs are removed.
@param session The TensorFlow session to be frozen.
@param keep_var_names A list of variable names that should not be frozen,
                    or None to freeze all the variables in the graph.
@param output_names Names of the relevant graph outputs.
@param clear_devices Remove the device directives from the graph for better portability.
@return The frozen graph definition.
"""
from tensorflow.python.framework.graph_util import convert_variables_to_constants
graph = session.graph
with graph.as_default():
    freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
    output_names = output_names or []
    output_names += [v.op.name for v in tf.global_variables()]
    # Graph -> GraphDef ProtoBuf
    input_graph_def = graph.as_graph_def()
    if clear_devices:
        for node in input_graph_def.node:
            node.device = ""
    frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                output_names, freeze_var_names)
    return frozen_graph

如何使用Intel推理引擎（OpenVINO）使用batchnorm层优化keras模型？

1 个答案: